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1

Crosstab/Contingency table

slices data by 2 categorical variables, bivariate

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2

Bar chart

utilizes frequency table and categorical data, univariate(one variable), uses height/length

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3

Histogram

usually used for continuous (numerical) data, univariate(one variable), uses height/length

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4

Positively/Right skewed

heavy to left side, light on right side (TAIL ON RIGHT)

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5

Negatively/Left skewed

heavy to right side, light on left side (TAIL ON LEFT)

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6

Stacked bar/column chart

utilizes contingency chart, can have both data types, usually categorical though, multivariate

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7

Line chart

measures two 2 things over time

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8

Scatterplot

relationship between 2 numeric variables/ the third variable can be categorical with a legend, uses position, can be bivariate or multivariate

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9

Business analytics

data analyses for business applications

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10

Data science

develop applications for end users

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11

Sequence/Types of Analytics

Descriptive → Diagnostic → Predictive → Prescriptive

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12

Statistical inference

is the process of using data from a sample to gain

information about the population

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13

Sampling bias

occurs when the method of selecting a sample causes the sample to differ from the population in some relevant way

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14

Time series data

data values observed over time

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15

Cross sectional data

values observed at the same point in time

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16

Structured Data

Reside in a pre-defined, row-column format; Spreadsheet or database applications; Enter, store, query, and analyze

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17

Unstructured Data

Do not conform to a pre-defined, row-column format; Textual; Multimedia content

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18

Discrete data

numerical, can have decimals, more strigid, would be a more jagged graph

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19

Continuous data

numerical, yes to decimals, what is the number above 1

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20

How to determine if it’s numerical or categorical

if you can perform a relevant calculation then it’s numerical ex: avg/mean (you don’t need the avg of zip codes, so it’s nominal)

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21

Nominal data

categorical, no order, can be numeric but usually words ex: 1=yes 0=no, uniform numbers, zip code

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22

Ordinal data

ranked, not necessarily a preference, ORDER

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23

Lollipop chart

variation of a bar chart, uses height/length

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24

Bullet graph

Encodes data using length/height, position and color to show actual compared to target and performance bands

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25

Dot plot

is a Univariate plot for Continuous data, uses position

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26

Box and whisker plot

univariate, for continuous data, uses position and height/length

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27

Pie chart

uses angle, area and arc to show a part-to-whole comparison, univariate, can be categorical or continuous

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28

Line chart

uses position and often shows trend over time, usually bivariate, time usually on x-axis and y-axis is usually numerical

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29

Sparkline / Sparkbar

using position (line) or height/length (bar) in a small, word-sized graphic

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30

Bubble plot

Allows to add more variables to scatter plot, can use color and size to visualize other (likely numerical) data, multivariate

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31

Heat Map

uses color, uses numerical data but does not use numbers in the visualization, bivariate

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32

Visual perception

the brain's ability to receive, interpret, and act upon visual stimuli

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33

Preattentive attributes

visual properties that we notice without using conscious effort to do so

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34

Important preattentive attributes used in graphs

Length, width, orientation (is it a different way than the others), size, shape, color hue, color intensity, position, texture

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35

Marks to encode quantitative values

Points, lines, bars, boxes, shapes with 2-d areas, shapes with color intensity

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36

Encoding categorical items

Hue, point shape, 2d position

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37

Pie charts are

bad! we don’t like to use them

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38

Business intelligence

Data + tools + brains

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39

As data analytics changes from descriptive to diagnostic to predictive to prescriptive, more human input is required for making decisions and enacting them.

FALSE

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40

The use of historical information to predict what could happen in the future describes prescriptive analytics.

FALSE - predictive analytics

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41

Social media data, such as Facebook, Instagram, and TicTok are examples of structured data.

FALSE

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42

Supervised learning

Input & output data, classification, regression, predictive and prescriptive models

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43

Unsupervised learning

Input data, clustering, association, PATTERN/structure discovery

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44

Four Vs of Big Data

velocity, variety, volume, veracity(accuracy of data)

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45

Descriptive Data Analytics

What is happening in my business?

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46

Diagnostic Data Analytics

Why is it happening?

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47

Prescriptive Data Analytics

What should be done?

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48

Predictive Data Analytics

What will happen in the future?

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49

Data analytics

the science of examining raw data to conclude that information; the process of inspecting, cleansing, transforming, and modeling data to discover useful information for decision-making.

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50

Big Data

massive complex structured and unstructured data sets that are rapidly generated and transmitted from a wide variety of sources

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51

Data Mining

a set of statistical and machine learning methods that inform decision-making. (Dipping through vast stores of data in search of something interesting)

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52

Information

a set of data that are organized and processed in a meaningful and purposeful way.

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